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MutPred Splice | Machine learning-based prediction of exonic variants that disrupt splicing

A machine-learning approach for the identification of coding region substitutions that disrupt pre-mRNA splicing. Applying MutPred Splice to human disease-causing exonic mutations suggests that 16% of mutations causing inherited disease and 10 to 14% of somatic mutations in cancer may disrupt pre-mRNA splicing.

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MutPred Splice classification

MutPred Splice specifications

Unique identifier:
OMICS_02257
Restrictions to use:
None
Computer skills:
Basic
Stability:
Stable
Interface:
Web user interface
Input format:
VCF
Version:
1.3.2
Maintained:
Yes

MutPred Splice support

Maintainer

  • Matthew Mort <>

Credits

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Publications

Institution(s)

Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK; Buck Institute for Research on Aging, Novato, CA, USA; Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA; Department of Molecular, Cellular and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA, USA; Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, USA

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